Equivalent roles of marine subsidies and island characteristics in shaping island bird communities
Bibliographic record
Abstract
Abstract Aim Species distributions across islands are shaped by dispersal limitations, environmental filters and biotic interactions but the relative influence of each of these processes has rarely been assessed. Here, we examine the relative contributions of island characteristics, marine subsidies, species traits, and species interactions on avian community composition. Location Central Coast region of British Columbia, Canada. Taxon Terrestrial breeding birds. Methods We observed 3610 individuals of 32 bird species on 89 islands that spanned multiple orders of magnitude in area (0.0002–3 km 2 ). We fit a spatially explicit joint species distribution model to estimate the relative contributions of island physical characteristics, island‐specific inputs of marine subsidies, species' traits, and biotic interactions on species distributions. Biogeographic characteristics included island area, isolation, and habitat heterogeneity, while marine influence was represented by forest‐edge soil δ 15 N, wrack biomass, shoreline substrate, and distance to shore. This approach also allowed us to estimate how much variation in distributions resulted from species' biological traits (i.e. body mass, feeding guild, feeding height, and nesting height). Results Bird species distributions were determined almost equivalently by island biogeographic characteristics (23.5% of variation explained) and marine influence (24.8%). We detected variation in species‐specific responses to both island biogeographic characteristics and marine influence, but no significant effect of any biological trait examined. Additionally, we found evidence that habitat preferences were a more important driver than competitive interactions. Main Conclusions Although most island biogeographic studies focus only on islands' physical characteristics, we found evidence for an equivalent role of marine subsidy in structuring island bird communities. Our study suggests that for small islands, disentangling the effects of island biogeographic characteristics, marine inputs, and biotic interactions is a useful next step in understanding species distributions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".